Thoughts On The Design Of The Fossil DVCS

Thoughts On The Design Of The Fossil DVCS

Two questions (or criticisms) that arise frequently regarding Fossil
can be summarized as follows:

Why is Fossil based on SQLite instead of a distributed NoSQL database?

Why is Fossil written in C instead of a modern high-level language?

Neither question can be answered directly because they are both
based on false assumptions. We claim that Fossil is not based on SQLite
at all and that Fossil is not based on a distributed NoSQL database
because Fossil is a distributed NoSQL database. And, Fossil does use
a modern high-level language for its implementation, namely SQL.

Fossil Is A NoSQL Database

We begin with the first question: Fossil is not based on a distributed
NoSQL database because Fossil is a distributed NoSQL database.
Fossil is not based on SQLite.
The current implementation of Fossil uses
SQLite as a local store for the content of the distributed database and as
a cache for meta-information about the distributed database that is precomputed
for quick and easy presentation. But the use of SQLite in this role is an
implementation detail and is not fundamental to the design. Some future
version of Fossil might do away with SQLite and substitute a pile-of-files or
a key/value database in place of SQLite.
(Actually, that is very unlikely
to happen since SQLite works amazingly well in its current role, but the point
is that omitting SQLite from Fossil is a theoretical possibility.)

The underlying database that Fossil implements has nothing to do with
SQLite, or SQL, or even relational database theory. The underlying
database is very simple: it is an unordered collection of "artifacts".
An artifact is a list of bytes - a "file" in the usual manner of thinking.
Many artifacts are simply the content of source files that have
been checked into the Fossil repository. Call these "content artifacts".
Other artifacts, known as
"control artifacts", contain ASCII text in a particular format that
defines relationships between other artifacts, such as which
content artifacts that go together to form a particular version of the
project. Each artifact is named by its SHA1 hash and is thus immutable.
Artifacts can be added to the database but not removed (if we ignore
the exceptional case of shunning.) Repositories
synchronize by computing the union of their artifact sets. SQL and
relation theory play no role in any of this.

SQL enters the picture only in the implementation details. The current
implementation of Fossil stores each artifact as a BLOB in an SQLite
database.
The current implementation also parses up each control artifact as it
arrives and stores the information discovered from that parse in various
other SQLite tables to facilitate rapid generation of reports such as
timelines, file histories, file lists, branch lists, and so forth. Note
that all of this additional information is derived from the artifacts.
The artifacts are canonical. The relational tables serve only as a cache.
Everything in the relational tables can be recomputed
from the artifacts, and in fact that is exactly what happens when one runs
the "fossil rebuild" command on a repository.

So really, Fossil works with two separate databases. There is the
bag-of-artifacts database which is non-relational and distributed (like
a NoSQL database) and there is the local relational database. The
bag-of-artifacts database has a fixed format and is what defines a Fossil
repository. Fossil will never modify the file format of the bag-of-artifacts
database in an incompatible way because to do so would be to make something
that is no longer "Fossil". The local relational database, on the other hand,
is a cache that contains information derived from the bag-of-artifacts.
The schema of the local relational database changes from time to time as
the Fossil implementation is enhanced, and the content is recomputed from
the unchanging bag of artifacts. The local relational database is an
implementation detail which currently happens to use SQLite.

Another way to think of the relational tables in a Fossil repository is
as an index for the artifacts. Without the relational tables,
to generate a report like a timeline would require scanning every artifact -
the equivalent of a full table scan. The relational tables hold pointers
the relevant artifacts in presorted order so that generating a timeline
is much more efficient. So like an index in a relational database, the
relational tables in an Fossil repository do not add any new information,
they merely make the information in the artifacts faster and easier to
look up.

Fossil is not "based" on SQLite. Fossil simply exploits SQLite as
a powerful tool to make the implementation easier.
And Fossil doesn't use a distributed
NoSQL database because Fossil is a distributed NoSQL database. That answers
the first question.

SQL Is A High-Level Scripting Language

The second concern states that Fossil does not use a high-level scripting
language. But that is not true. Fossil uses SQL (as implemented by SQLite)
as its scripting language.

This misunderstanding likely arises because people fail
to appreciate that SQL is a programming language. People are taught that SQL
is a "query language" as if that were somehow different from a
"programming language". But they really are two different flavors of the
same thing. I find that people do better with SQL if they think of
SQL as a programming language and each statement
of SQL is a separate program. SQL is a peculiar programming language
in that one uses SQL to specify what to compute whereas in
most other programming languages one specifies how
to carry out the computation.
This difference means that SQL
is an extraordinary high-level programming language, but it is still
just a programming language.

For certain types of problems, SQL has a huge advantage over other
programming languages because it is so high level and because it allows
programmers to focus more on the what and less of the how
of a computation. In other words,
programmers tend to think about problems at a much higher level when
using SQL; this can result in better applications.
SQL is also very dense.
In practice, this often means that a few
lines of SQL can often replace hundreds or thousands of lines of
procedural code, with a corresponding decrease in programming effort
and opportunities to introduce bugs.
Fossil happens to be one of those problems for which SQL is well suited.

Much of the "heavy lifting" within the Fossil implementation is carried
out using SQL statements. It is true that these SQL statements are glued
together with C code, but it turns out that C works surprisingly well in
that role. Several early prototypes of Fossil were written in a scripting
language (TCL). We normally find that TCL programs are shorter than the
equivalent C code by a factor of 10 or more. But in the case of Fossil,
the use of TCL was actually making the code longer and more difficult to
understand.
And so in the final design, we switched from TCL to C in order to make
the code easier to implement and debug.

Without the advantages of having SQLite built in, the design might well
have followed a different path. Most reports generated by Fossil involve
a complex set of queries against the relational tables of the repository
database. These queries are normally implemented in only a few dozen
lines of SQL code. But if those queries had been implemented procedurally
using a key/value or pile-of-files database, it
may have well been the case that a high-level scripting language such as
Tcl, Python, or Ruby may have worked out better than C.

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